Understanding how cortical circuits process sensory information and support perception is a fundamental problem in neuroscience. Rodents, being tactile experts, actively use their whiskers to sense complex tactile features like surface texture, object shape and location. In this dissertation I address how cortical neurons integrate sensory information from individual whiskers to support accurate and precise representations of complex tactile features.

Natural whisking during tactile exploration generates complex spatiotemporal sequences of whisker stimulation. Objects with different textures and shapes result in different patterns of whisker stimulation, sequentially stimulating different combinations of whiskers across time. I thus hypothesized that individual neurons in primary whisker somatosensory cortex (S1) are sensitive to specific features of tactile sequences. Chapter 2 describes the timescales at which S1 neurons integrated sensory input while rats discriminated between whisker impulse sequences that varied in single-impulse kinematics. While discrimination performance was consistent with integration at a relatively slow timescale (approximately 150ms), most S1 neurons integrated whisker input at a fast timescale (<20ms), generating a precise code for vibrotactile sequences in S1. Neurons with slower integration windows (>60ms) did not accurately represent the stimulus but were instead related to the rat’s behavioral choice. These findings show that S1 neurons encode whisker input at a fast timescale and suggest that areas downstream of S1 temporally integrate this information to guide perceptual discrimination.

Given the precise representations of tactile sequences in S1, Chapter 3 explores the elementary computations underlying tactile stimulus integration by S1 neurons. Tactile sequences vary in spatial identity of stimulated whiskers and inter-whisker-deflection-intervals (Δt). Dense stimulation of local whisker pairs over a physiological range of Δt revealed a somatopically organized rate code for whisker combinations that was precise in space and coarser in time. Sublinear suppression for suboptimal combinations sharpened tuning relative to that expected from linear integration alone; analogous to the computation of motion direction selectivity in many visual circuits, thus suggesting a common computation for spatiotemporal feature extraction. Taken together, this dissertation shows that S1 neurons integrate sensory input in space and time to generate robust tuning for spatiotemporal features of tactile scenes.